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Distributed storage of manufacturing process measurement data based on database tag-aware sharding

Published:2024-01-29 author:WANG Quanshen, ZHANG Aimei. Browse: 289 Check PDF documents

Distributed storage of manufacturing process measurement data based on

database tag-aware sharding

WANG Quanshen, ZHANG Aimei

(School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

Abstract: In order to solve the problem of data association and aggregation query between multiple measuring instruments during the online measurement process for parts, the distributed storage and sharing methods of measuring were studied. Firstly, non-relational database was used in multiple measuring instrument cluster, which realized distributed storage of measuring data and simplified parts data storage model. Then, tag aware sharding model was used to classify and store the measurement data of different processes. Finally, multiple Raspberry Pi 3 Model B+ development boards with embedded Linux were used as the system platforms for the measurement instruments, and MongoDB database was used as online measurement distributed data storage to verify the uniformity and stability of distributed data storage on each node using a large amount of test data. The cluster had been integrated with manufacturing execution system (MES), which could monitor and analyze the measuring data of parts in real time, and quickly summarize the working procedure data in all measuring nodes to generate the report forms. The research results show that the single-node query response speed is stable in the range of 125ms to 208ms when the data volume reaches 7.2×105 in the cluster, which is 88.15% higher than that of the hashed sharding model, at the same time, the response speed of multi-node cooperative query is 1308ms, which is 61.64% faster than the scheme of “ascending key + searching key”. Aggregating and summarizing 1×105 parts data within multiple measurement nodes takes approximately 5s. This storage cluster can monitor the production status of parts and plays an important role in improving manufacturing efficiency and quality.

Key words: online measurement of parts; distributed data storage; tag-aware sharding; MongoDB; embedded measuring instrument; manufacturing execution system(MES)

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